Model Brainv0.1.2

A financial assistant chatbot uses model-brain to route customer queries to the cheapest capable model. Casual chat goes to minimax-m2.5, while high-stakes account transfers use claude-opus-4.6, reducing costs without sacrificing safety.

A development platform integrates model-brain to route code-related requests to gpt-5.2-codex for implementation tasks, while documentation and planning go to cheaper models, optimizing AI spend across the engineering team.

A crypto wallet app employs model-brain to automatically escalate transaction requests involving large sums to claude-opus-4.6, while routine balance checks are handled by claude-sonnet-4.5, ensuring security without excessive cost.

A legal research tool uses model-brain to send long, complex documents to gemini-3-pro for synthesis, while simple summarization tasks use minimax-m2.5, improving throughput and reducing latency for users.

An automation platform leverages model-brain to detect deterministic tasks—like scheduling or data entry—and route them to zero-LLM paths, avoiding unnecessary LLM calls and saving on compute costs.

Charge clients based on the number of routed messages, with pricing tiers depending on the model used. Provides predictable revenue while encouraging cost-conscious model selection.

Offer tiered plans that vary by routing complexity and access to premium models (e.g., claude-opus-4.6). Basic plans use zero-LLM and cheap models; enterprise plans include high-stakes escalation.

Price the service as a percentage of the cost savings achieved by optimizing model selection compared to using a single expensive model. Aligns incentives with client ROI.